Respiratory rate detection using decomposition of ECG

ABSTRACT

A method and system for determining a respiratory rate of a user using an electrocardiogram (ECG) segment of the user are disclosed. The method comprises decomposing the ECG segment into a plurality of functions and evaluating the plurality of functions to choose one of the plurality of functions based on a respiratory band power. The method includes determining the respiratory rate using the one of the plurality of functions and a domain detection.

This application is a Divisional of U.S. application Ser. No.13/487,022, filed Jun. 1, 2012, which is incorporated herein byreference in its entirety.

FIELD OF THE INVENTION

The present invention relates to sensor devices, and more particularly,to a sensor device utilized to determine respiratory rate usingdecomposition of an ECG.

BACKGROUND

A person's respiratory rate measures the number of breaths taken withina predetermined time period, typically 60 seconds. The respiration rateis one of the vital signs commonly used in clinical practices. Severalpathological conditions such as sleep apnea and chronic obstructivepulmonary disease are associated with respiratory dysfunction and/orabnormal respiratory patterns. A person's age, physical condition, andmedical history all have a direct effect on the ability to maintain anormal respiratory rate. Precise monitoring of a person's respiratoryrate is crucial to identifying potential markers in the person'sdiagnosis and prognosis in various clinical settings.

Conventional methods of respiratory rate measurements include listeningto lung sounds using stethoscopes, spirometry, capnography, inductanceplethysomography, impedance pneumography, and thermistors. The drawbacksof these conventional methods include being expensive, invasive,cumbersome, inefficient, and inaccurate. In addition, these intrusivedevices also interfere with natural physiological breathing patterns.Moreover, these conventional methods, while suitable for point-of-careapplications, are not suitable for remote sensing, telehealthmonitoring, and home-based, long-term monitoring applications.

These issues limit the continuous remote monitoring of a person'srespiratory rate. Therefore, there is a strong need for a cost-effectivesolution that overcomes the above issues by non-invasively calculatingrespiratory rate in real-time using sensor devices. The presentinvention addresses such a need.

SUMMARY OF THE INVENTION

A method and system for determining a respiratory rate of a user usingan electrocardiogram (ECG) segment of the user are disclosed. The methodcomprises decomposing the ECG segment into a plurality of functions andevaluating the plurality of functions to choose one of the plurality offunctions based on a respiratory band power. The method includesdetermining the respiratory rate using the one of the plurality offunctions and a domain detection.

In a second aspect, the system comprises a wireless sensor device with aprocessor and a memory device coupled to the processor, wherein thememory device stores an application which, when executed by theprocessor, causes the processor to decompose the ECG segment into aplurality of functions and to evaluate the plurality of functions tochoose one of the plurality of functions based on a respiratory bandpower. The application further causes the processor to determine therespiratory rate using the one of the plurality of functions and adomain detection.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures illustrate several embodiments of the inventionand, together with the description, serve to explain the principles ofthe invention. One of ordinary skill in the art will recognize that theparticular embodiments illustrated in the figures are merely exemplary,and are not intended to limit the scope of the present invention.

FIG. 1 illustrates a diagram of the respiratory effects on the ECG inaccordance with an embodiment.

FIG. 2 illustrates a wireless sensor device in accordance with anembodiment.

FIG. 3 illustrates a flow chart of a method in accordance with anembodiment.

FIG. 4 illustrates a diagram of a decomposition of the ECG in accordancewith an embodiment.

FIG. 5 illustrates a diagram of a comparison between a true respirationsignal and ECG decomposed respiration signal in accordance with anembodiment.

FIG. 6 illustrates a diagram of respiratory rate extraction inaccordance with an embodiment.

FIG. 7 illustrates a diagram of a time-domain based peak detectionalgorithm in accordance with an embodiment.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The present invention relates to sensor devices, and more particularly,to a sensor device utilized to determine respiratory rate usingdecomposition of an electrocardiogram (ECG). The following descriptionis presented to enable one of ordinary skill in the art to make and usethe invention and is provided in the context of a patent application andits requirements. Various modifications to the preferred embodiment andthe generic principles and features described herein will be readilyapparent to those skilled in the art. Thus, the present invention is notintended to be limited to the embodiments shown but is to be accordedthe widest scope consistent with the principles and features describedherein.

A method and system in accordance with the present invention allows forthe measurement of a user's respiratory rate using a sensor device viadecomposition of an ECG data signal segment. By connecting a sensordevice to the user through two or more skin contacting sensor nodes orelectrodes, an ECG data signal segment of the user can be measured. TheECG data signal segment is then processed to eliminate artifacts andnoise and decomposed into Intrinsic Mode Functions (IMFs) using EnsembleEmpirical Mode Decomposition (EEMD). These IMFs are evaluated and basedupon a spectral content of the respiratory band power, the respiratoryrate of the user is determined using time and frequency domains.

One of ordinary skill in the art readily recognizes that a variety ofsensor devices can be utilized for the measuring of the ECG data signalsegment including portable wireless sensor devices with embeddedcircuitry in a patch form factor and that would be within the spirit andscope of the present invention.

To describe the features of the present invention in more detail, refernow to the following description in conjunction with the accompanyingFigures.

FIG. 1 illustrates a diagram 100 of the respiratory effects on the ECGin accordance with an embodiment. One of ordinary skill in the artreadily recognizes that in addition to decomposition of the ECG datasignal segment, a variety of ECG based algorithms can be utilized tomeasure the respiratory effects on the ECG including but not limited toband pass filtering, analyzing the QRS area, R amplitude, RS amplitude,and heart rate variability and that would be within the spirit and scopeof the present invention. These ECG based algorithms require highersampling rates of at least 500 Hertz (Hz) and are limited by therobustness of the R wave detection and the presence of pacemakers and/orabnormal heart rhythms.

FIG. 2 illustrates a wireless sensor device 200 in accordance with anembodiment. The wireless sensor device 200 includes a sensor 202, aprocessor 204 coupled to the sensor 202, a memory 206 coupled to theprocessor 204, an application 208 coupled to the memory 206, and atransmitter 210 coupled to the application 208. The wireless sensordevice 200 is attached, in any orientation, to a user. The sensor 202obtains data from the user and transmits the data to the memory 206 andin turn to the application 208. The processor 204 executes theapplication 208 to determine information regarding an ECG of the userand to subsequently determine a respiratory rate of the user. Theinformation is transmitted to the transmitter 210 and in turn relayed toanother user or device.

One of ordinary skill in the art readily recognizes that the wirelesssensor device 200 can utilize a variety of devices for the sensor 202including but not limited to uni-axial accelerometers, bi-axialaccelerometers, tri-axial accelerometers, gyroscopes, pressure sensors,photoplethysmograph (pulse oximeter sensors), and electrodes and thatwould be within the spirit and scope of the present invention. One ofordinary skill in the art readily recognizes that the wireless sensordevice 200 can utilize a variety of devices for the processor 204including but not limited to microprocessors, controllers, andmicrocontrollers and that would be within the spirit and scope of thepresent invention. In addition, one of ordinary skill in the art readilyrecognizes that a variety of devices can be utilized for the memory 206,the application 208, and the transmitter 210 and that would be withinthe spirit and scope of the present invention.

One of ordinary skill in the art readily recognizes that the informationregarding an ECG of the user can be different types of informationincluding but not limited to an ECG data signal segment and that wouldbe within the spirit and scope of the present invention. Additionally,one of ordinary skill in the art readily recognizes that the ECG datasignal segment can be measured at a variety of sampling frequencies andpredetermined time periods including but not limited to a 125 Hzsampling frequency (F_(s)) and a predetermined time period length of 40seconds and that would be within the spirit and scope of the presentinvention.

FIG. 3 illustrates a flow chart of a method 300 in accordance with anembodiment. Referring to FIGS. 2 and 3 together, an ECG segment of auser measured by the sensor 202 is decomposed into a plurality offunctions by the application 208, via step 302. Each of the plurality offunctions is evaluated by the application 208 to choose one of theplurality of functions based on a respiratory band power, via step 304.The respiratory rate is determined by the application 208 using the oneof the plurality of functions and a domain detection, via step 306. Oneof ordinary skill in the art readily recognizes that the application 208can be different types of executable applications and that would bewithin the spirit and scope of the present invention.

One of ordinary skill in the art readily recognizes that before the ECGsegment measured by the sensor 202 is decomposed into a plurality offunctions by the application 208, it can be pre-processed to eliminatenoise and artifacts utilizing a variety of mechanisms including but notlimited to using any combination of a low pass digital filter with acutoff frequency (F_(c)) of 10 Hz and a down sampling with a F_(s) of 25Hz and that would be within the spirit and scope of the presentinvention.

In one embodiment, the ECG signal segment x(t) is decomposed into aplurality of Intrinsic Mode Functions (IMFs) using an Ensemble EmpiricalMode Decomposition (EEMD) algorithmic process. Empirical modedecomposition (EMD) is a nonlinear, adaptive time-frequency analysistechnique and is a fully data driven tool that decomposes an originalsignal into fast/high and slow/low oscillations/frequencies indecreasing fashion. The EMD algorithm decomposes a given ECG signalsegment x(t) into a set of zero-mean amplitude modulation (AM) andfrequency modulation (FM) components called Intrinsic Mode Functions(IMFs) or oscillatory modes. Each IMF is defined as a function that mustsatisfy two conditions: a) in the whole data set, |(#of Extrema)−(#ofZeroes)|<=1, and b) at any time point of the signal, the mean value ofmax envelope and min envelope is zero. The EMD algorithm is representedby the following equation, where d_(k)(t) is the IMF, K is the number ofmodes, r(t) is a residual trend, and k=1, 2 . . . K:x(t)=Σ(d _(k)(t)+r(t)),where Σ is from k=1 to K.

One of ordinary skill in the art readily recognizes that the EMDalgorithm experiences mode mixing that is eliminated utilizing EEMD andthat would be within the spirit and scope of the present invention. EEMDis a noise-assisted data analysis method that determines the true IMFcomponents as the mean of a number of ensemble trials in which thesignal is added with white noise of finite amplitude. The differentrealizations of white noise cancel each other out in time-space ensemblemean. Therefore, only the true IMF components of ECG signal segment x(t)survive in the white noise-added signal ensemble mean.

Additionally, one of ordinary skill in the art readily recognizes thatthe amplitude of the added white noise, the number of ensemble trials(or realization index), and number of sifting operations can be avariety of selections including but not limited to 0.2*standarddeviation of the signal for the amplitude of the added white noise, 100for the number of ensemble trials, and 10 for the number of siftingoperations and that would be within the spirit and scope of the presentinvention.

In this embodiment, the EEMD algorithmic process adds white noisew_(i)(t) to the target ECG segment data x(t) to produce a new signalx_(i)(t) represented by the following equation:x _(i)(t)=x(t)+w _(i)(t),where i is the realization index(i=1, . . .,100).Let j=k=0, h_(i,j,k)(t)=x_(i)(t), and r_(i,k)(t)=x_(i)(t). The signalh_(i,j,k)(t) is decomposed using EMD which includes the steps of: a)identifying all the extrema (minima and maxima) of h_(i,j,k)(t), b)obtaining the respective envelopes e_(min)(t) and e_(max)(t) via cubicspline interpolation, c) computing the mean envelopem(t)=((e_(min)(t)+e_(max)(t))/2, d) extracting the detailh_(i,j+1,k)(t)=h_(i,j,k)(t)−m(t), and repeating the above steps a-d onh_(i,j+1,k)(t) with j←j+1 until j=9 (e.g., 10 repetitions); each repeatis known as a sifting operation. One of ordinary skill in the artreadily recognizes that the above steps a-d can be repeated a differentnumber of times and that would be within the spirit and scope of thepresent invention.

In step e), each effective IMF is determined asd_(i,k+1)(t)=h_(i,10,k)(t) where h_(i,10,k)(t) is the extracted detail.In step f), the residual r_(i,k+1)(t) is obtained by subtracting thiseffective IMF from the new signal as represented by the followingequation: r_(i,k+1)(t)=r_(i,k)(t)−d_(i,k+1)(t). In step g) set j=0,consider the new input signal h_(i,j,k+1)(t)=r_(i,k+1)(t), set k←k+1,and repeat steps a to f. The steps a to f are repeated K times (e.g.,k=0, . . . , K−1) where K=log₂(N)−1 and N is the length of the data. Asa result, the respective IMFs d_(i,k)(t) and residues r_(i,k)(t), k=1, .. . , K for each realization i are obtained. In step h) the realizationindex ‘i’ is incremented (i←i+1) and the indexes j and k are reset (j=0,k=0). The above-mentioned steps a-g are then repeated until i=100. As aresult of this process, the following signals are generated: noisy ECGsignals x_(i)(t), where i=1, . . . , 100, noisy IMFs d_(i,k)(t), andnoisy residues r_(i,k)(t), where i=1, . . . , 100 and k=1, . . . , K.Finally, the true IMFs d_(k)(t) and residue r_(k)(t) are determined bycalculating the ensemble means of d_(i,k)(t) and r_(i,k)(t) per thefollowing equations:

${d_{k}(t)} = {{\frac{1}{100}{\sum\limits_{i = 1}^{100}{{d_{i,k}(t)}\mspace{14mu}{and}\mspace{14mu}{r(t)}}}} = {\frac{1}{100}{\sum\limits_{i = 1}^{100}{{r_{i,K}(t)}.}}}}$

In one embodiment, once a plurality of true IMFs are determined, thespectral content of each of the plurality of true IMFs is evaluated byobtaining a Power Spectral Density (PSD) for each of the plurality oftrue IMFs. In one embodiment, the PSD is obtained using the Welchperiodogram algorithm. The respiratory band power for each of theplurality of true IMFs is obtained as the area under the PSD curvebetween certain frequencies (e.g., frequencies from 0.11 Hz to 0.45 Hz).One of ordinary skill in the art readily recognizes that the respiratoryband power obtained for each of the plurality of IMFs can be a varietyof ranges including but not limited to a range between 0.11 Hz to 0.45Hz and that would be within the spirit and scope of the presentinvention.

The total respiratory band power is obtained as the sum of each IMF'srespiratory band power. Then the ratio of individual respiratory bandpower to the total respiratory band power is obtained as a percentage.Finally, the specific true IMF that contributes maximal percentage ofthe total respiratory band power of the plurality of true IMFs isselected as a surrogate respiration waveform.

Using the specific true IMF that is selected as the surrogaterespiration waveform, the breath-to-breath respiratory rate of the useris determined in the time-domain as (1/respiration interval inseconds×60) using an algorithm that can identify peak events with thecomputation of a first derivative of the surrogate respiration waveformand can find the zero crossing events with positive to negative signchange. One of ordinary skill in the art readily recognizes that thistime-domain based algorithm can be a variety of algorithmic forms andthat would be within the spirit and scope of the present invention.

FIG. 7 illustrates a diagram 700 of a time-domain based peak detectionalgorithm that extracts breath-to-breath respiratory rates from the ECGdecomposed respiratory surrogate waveform. In FIG. 7 , the identifiedpeak events in a sample ECG decomposed surrogate respiratory waveformare illustrated with black circles filled in green color. The timedifference between two consecutive peaks, denoted by the arrow 702, isfound to be a breathing time interval of 4.04 seconds which correspondsto (1/4.04)*60 or approximately 14.85 breaths per minute. From thesebreath-to-breath respiratory rates, the average respiratory rate can befound for a predetermined time period data window including but notlimited to 40 seconds.

On the other hand, the average respiratory rate for the predeterminedtime period data window of 40 seconds in the current selection can bedetermined in the frequency-domain from the PSD of the surrogaterespiration waveform by determining a peak frequency with a maximal PSDin the frequency band of 0.11 Hz to 0.45 Hz. In one embodiment, the ECGsignal segment is shifted every 5 seconds to provide a 35 second overlapfor increased precision in determining the respiratory rate. One ofordinary skill in the art readily recognizes that a variety of shiftingtimes can be utilized and that would be within the spirit and scope ofthe present invention.

FIG. 4 illustrates a diagram 400 of a decomposition of the ECG inaccordance with an embodiment. The diagram 400 includes an ECG segment402 that is low pass filtered with a cutoff frequency of 10 Hz and laterresampled at 25 Hz, a plurality of true IMFs 404, and a residual 406obtained utilizing the aforementioned EEMD decomposition algorithmicprocess. One of ordinary skill in the art readily recognizes thatalthough 8 true IMFs are calculated, a variety of different number oftrue IMFs can be calculated based on the length of the predeterminedtime period data window to be analyzed and that would be within thespirit and scope of the present invention.

One of ordinary skill in the art readily recognizes that a variety ofcomparative data sets can be formulated to compare respiratory ratesignals that have been determined by conventional methods to therespiratory rate signals that have been determined by the presentinvention and that would be within the spirit and scope of the presentinvention. Additionally, one of ordinary skill in the art readilyrecognizes that the respiration rate detection algorithm can be appliedto short-term (e.g., a few minutes to a few hours) as well as long-term(e.g., a 24 hour period to a few days) ECG monitoring applications andthat would be within the spirit and scope of the present invention.

FIG. 5 illustrates a diagram 500 of a comparison between a truerespiration signal and ECG decomposed respiration signal in accordancewith an embodiment. The diagram 500 includes a data set 502 displaying atrue respiration signal, a data set 504 displaying that the truerespiration signal's frequency was determined to be 0.244 Hz from PSD, adata set 506 displaying an ECG decomposed respiration (EDR) signalobtained by the present invention, and a data set 508 displaying thatthe EDR signal's frequency was also determined to be 0.244 Hz.Accordingly, the EDR signal's frequency was determined to be the same asthe true respiration signal's frequency. As shown in FIG. 5 , the peakfrequency with the maximal PSD in the frequency band of 0.11 Hz to 0.45Hz is found to be 0.244 Hz which correspond to approximately 14.65breaths/min.

FIG. 6 illustrates a diagram 600 of respiratory rate extraction inaccordance with an embodiment. The diagram 600 includes a data set 602displaying the ECG data, the respiration data, and the frequency valuesof a respiration rate extraction for data compiled over a 30 minuteperiod and a zoomed in view 604 of the data set 602 that spans a 1minute period and that shows 100% accuracy of the EDR signal obtained bythe present invention.

As above described, the method and system allow for determining arespiratory rate of a user using an electrocardiogram (ECG) segment ofthe user. By attaching a wireless sensor device to the user to measure adata signal representing an ECG segment, processing the resultant datasignal, decomposing the processed signal into Intrinsic Mode Functions(IMFs), and evaluating these IMFs based upon a spectral content of therespiratory band power, an accurate respiratory rate of the user can becalculated utilizing a non-invasive and efficient system.

A method and system for determining a respiratory rate of a user usingan electrocardiogram (ECG) segment of the user has been disclosed.Embodiments described herein can take the form of an entirely hardwareimplementation, an entirely software implementation, or animplementation containing both hardware and software elements.Embodiments may be implemented in software, which includes, but is notlimited to, application software, firmware, resident software,microcode, etc.

The steps described herein may be implemented using any suitablecontroller or processor, and software application, which may be storedon any suitable storage location or computer-readable medium. Thesoftware application provides instructions that enable the processor tocause the receiver to perform the functions described herein.

Furthermore, embodiments may take the form of a computer program productaccessible from a computer-usable or computer-readable storage mediumproviding program code or program instructions for use by or inconnection with a computer or any instruction execution system. For thepurposes of this description, a computer-usable or computer-readablestorage medium can be any apparatus that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.

The computer-readable storage medium may be an electronic, magnetic,optical, electromagnetic, infrared, semiconductor system (or apparatusor device), or a propagation medium. Examples of a computer-readablestorage medium include a semiconductor or solid state memory, magnetictape, a removable computer diskette, a random access memory (RAM), aread-only memory (ROM), a rigid magnetic disk, and an optical disk.Current examples of optical disks include DVD, compact disk-read-onlymemory (CD-ROM), and compact disk-read/write (CD-R/W).

Although the present invention has been described in accordance with theembodiments shown, one of ordinary skill in the art will readilyrecognize that there could be variations to the embodiments and thosevariations would be within the spirit and scope of the presentinvention. Accordingly, many modifications may be made by one ofordinary skill in the art without departing from the spirit and scope ofthe appended claims.

What is claimed is:
 1. A method to determine a respiratory rate of auser using a wireless sensor device that includes at least oneelectrode, a processor, a transmitter and a memory device coupled to theprocessor, the memory device stores executable instructions that, whenexecuted by the processor, causes the processor to perform operations,comprising: measuring an electrocardiogram (ECG) segment of the user viathe at least one electrode; decomposing the ECG segment into a pluralityof Intrinsic Mode Functions (IMFs) using Ensemble Empirical ModeDecomposition (EEMD); evaluating the plurality of IMFs to choose one ofthe plurality of IMFs based on a respiratory band power of the pluralityof IMFs; evaluating spectral content of each of the plurality of IMFs byobtaining a Power Spectral Density (PSD) for each of the plurality ofIMFs; obtaining the respiratory band power for each of the plurality ofIMFs; choosing one of the plurality of IMFs that contributes maximalpercentage of a total respiratory band power of the plurality of IMFs;providing the one of the plurality of IMFs as a surrogate respirationwaveform; and determining the respiratory rate using the one of theplurality of functions and a time-domain based peak detection algorithm.2. The method of claim 1, wherein the determining further comprises:determining the respiratory rate using breath-to-breath peak detectionin time-domain and respiratory frequency peak in the PSD of thesurrogate respiration waveform.
 3. The method of claim 2, wherein thebreath-to-breath peak detection in time-domain includes an algorithmthat identifies peak events by computing a first derivative of thesurrogate respiration waveform and finds zero crossing events withpositive to negative sign changes.
 4. The method of claim 1, wherein thePSD is obtained using a Welch periodogram.
 5. The method of claim 1,wherein the respiratory band power obtained for each of the plurality ofIMFs is an area under the PSD between frequencies 0.11 Hz to 0.45 Hz. 6.The method of claim 1, further comprising: processing the ECG segmentusing both a low pass digital filter with a cutoff frequency (F_(c)) of10 Hz and a down sampling with a sampling frequency (F_(s)) of 25 Hz. 7.The method of claim 1, wherein the ECG segment is measured at a 125 Hzsampling frequency (F_(s)) and at a length of 40 seconds.
 8. The methodof claim 7, further comprising: shifting the ECG segment every 5 secondsto provide a 35 second overlap for determining the respiratory rate. 9.A non-transitory computer-readable medium storing executableinstructions that, in response to execution, cause a wireless sensordevice that includes at least one electrode, a processor, a transmitterand a memory device coupled to the processor, to perform operations todetermine a respiratory rate of a user using an electrocardiogram (ECG)segment of the user comprising: measuring an electrocardiogram (ECG)segment of the user via the at least one electrode; decomposing the ECGsegment into a plurality of Intrinsic Mode Functions (IMFs) usingEnsemble Empirical Mode Decomposition (EEMD); evaluating the pluralityof IMFs to choose one of the plurality of IMFs based on a respiratoryband power of the plurality of IMFs; evaluating spectral content of eachof the plurality of IMFs by obtaining a Power Spectral Density (PSD) foreach of the plurality of IMFs; obtaining the respiratory band power foreach of the plurality of IMFs; choosing one of the plurality of IMFsthat contributes maximal percentage of a total respiratory band power ofthe plurality of IMFs; providing the one of the plurality of IMFs as asurrogate respiration waveform; and determining the respiratory rateusing the one of the plurality of functions and a time-domain based peakdetection algorithm.
 10. The non-transitory computer-readable medium ofclaim 9, wherein the determining further comprises: determining therespiratory rate using breath-to-breath peak detection in time-domainand respiratory frequency peak in the PSD of the surrogate respirationwaveform.
 11. The non-transitory computer-readable medium of claim 10,wherein the breath-to-breath peak detection in time-domain includes analgorithm that identifies peak events by computing a first derivative ofthe surrogate respiration waveform and finds zero crossing events withpositive to negative sign changes.
 12. The non-transitorycomputer-readable medium of claim 9, wherein the PSD is obtained using aWelch periodogram.
 13. The non-transitory computer-readable medium ofclaim 9, wherein the respiratory band power obtained for each of theplurality of IMFs is an area under the PSD between frequencies 0.11 Hzto 0.45 Hz.
 14. The non-transitory computer-readable medium of claim 9,further comprising: processing the ECG segment using both a low passdigital filter with cutoff frequency (F_(c)) of 10 Hz and a downsampling with a sampling frequency (F_(s)) of 25 Hz.
 15. Thenon-transitory computer-readable medium of claim 9, wherein the ECGsegment is measured at a 125 Hz sampling frequency (F_(s)) and at alength of 40 seconds.
 16. The non-transitory computer-readable medium ofclaim 15, further comprising: shifting the ECG segment every 5 secondsto provide a 35 second overlap for determining the respiratory rate.